{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "given-exhaust",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.531924Z",
     "iopub.status.busy": "2021-03-18T08:36:38.531441Z",
     "iopub.status.idle": "2021-03-18T08:36:38.534741Z",
     "shell.execute_reply": "2021-03-18T08:36:38.534087Z"
    },
    "papermill": {
     "duration": 0.055039,
     "end_time": "2021-03-18T08:36:38.534885",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.479846",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\" # CIFAR10, CIFAR100, SVHN\n",
    "v_type = \"mingd\" # mingd, rand\n",
    "root_path = \"\"\n",
    "params_path = \"\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "honest-embassy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.604797Z",
     "iopub.status.busy": "2021-03-18T08:36:38.604320Z",
     "iopub.status.idle": "2021-03-18T08:36:38.607716Z",
     "shell.execute_reply": "2021-03-18T08:36:38.607047Z"
    },
    "papermill": {
     "duration": 0.037466,
     "end_time": "2021-03-18T08:36:38.607859",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.570393",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR100\"\n",
    "v_type = \"rand\"\n",
    "root_path = \"/home/mohammad/transferability/files\"\n",
    "params_path = \"/home/mohammad/transferability/src\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "solar-chess",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.692617Z",
     "iopub.status.busy": "2021-03-18T08:36:38.691401Z",
     "iopub.status.idle": "2021-03-18T08:36:38.696560Z",
     "shell.execute_reply": "2021-03-18T08:36:38.697541Z"
    },
    "papermill": {
     "duration": 0.053277,
     "end_time": "2021-03-18T08:36:38.697838",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.644561",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# CIFAR100 - Blind Walk results"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "v_type2disp = dict(mingd=\"MinGD\", rand=\"Blind Walk\")\n",
    "display(Markdown(f\"# {dataset} - {v_type2disp[v_type]} results\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "inner-settle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.770770Z",
     "iopub.status.busy": "2021-03-18T08:36:38.770327Z",
     "iopub.status.idle": "2021-03-18T08:36:39.389146Z",
     "shell.execute_reply": "2021-03-18T08:36:39.388053Z"
    },
    "papermill": {
     "duration": 0.656272,
     "end_time": "2021-03-18T08:36:39.389418",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.733146",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(0, params_path)\n",
    "\n",
    "import argparse, params, time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from importlib import reload\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import random\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "naked-joining",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.460919Z",
     "iopub.status.busy": "2021-03-18T08:36:39.460427Z",
     "iopub.status.idle": "2021-03-18T08:36:40.084901Z",
     "shell.execute_reply": "2021-03-18T08:36:40.083830Z"
    },
    "papermill": {
     "duration": 0.658973,
     "end_time": "2021-03-18T08:36:40.085157",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.426184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "vulnerable-niger",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.157957Z",
     "iopub.status.busy": "2021-03-18T08:36:40.157501Z",
     "iopub.status.idle": "2021-03-18T08:36:40.159442Z",
     "shell.execute_reply": "2021-03-18T08:36:40.159773Z"
    },
    "papermill": {
     "duration": 0.036919,
     "end_time": "2021-03-18T08:36:40.159896",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.122977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import combine_pvalues, ttest_ind_from_stats, ttest_ind\n",
    "from functools import reduce\n",
    "from scipy.stats import hmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-threshold",
   "metadata": {
    "papermill": {
     "duration": 0.029022,
     "end_time": "2021-03-18T08:36:40.223942",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.194920",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Use the full vectors to train a linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "located-wages",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.290097Z",
     "iopub.status.busy": "2021-03-18T08:36:40.289652Z",
     "iopub.status.idle": "2021-03-18T08:36:40.291992Z",
     "shell.execute_reply": "2021-03-18T08:36:40.292330Z"
    },
    "papermill": {
     "duration": 0.038491,
     "end_time": "2021-03-18T08:36:40.292472",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.253981",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root = os.path.join(root_path,dataset)\n",
    "model_types = ['zero-shot', 'prune', 'fine-tune', 'extract-label', 'extract-logit', 'distillation', 'teacher']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tamil-monte",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.365734Z",
     "iopub.status.busy": "2021-03-18T08:36:40.365289Z",
     "iopub.status.idle": "2021-03-18T08:36:40.376130Z",
     "shell.execute_reply": "2021-03-18T08:36:40.376954Z"
    },
    "papermill": {
     "duration": 0.049815,
     "end_time": "2021-03-18T08:36:40.377212",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.327397",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\" and dataset == \"CIFAR100\": v_type = \"topgd\"\n",
    "    \n",
    "names = [\"teacher\",\"distillation\",\"extract-label\",\"extract-logit\",\"zero-shot\",\"fine-tune\",\"pre-act-18\",\"independent\"]\n",
    "trains = {}\n",
    "tests = {}\n",
    "for name in names:\n",
    "    trains[name] = (torch.load(f\"{root}/model_{name}_normalized/train_{v_type}_vulnerability.pt\"))\n",
    "    tests[name] = (torch.load(f\"{root}/model_{name}_normalized/test_{v_type}_vulnerability.pt\"))\n",
    "mean_cifar = trains[\"teacher\"].mean(dim = (0,1))\n",
    "std_cifar = trains[\"teacher\"].std(dim = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "russian-peoples",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.455970Z",
     "iopub.status.busy": "2021-03-18T08:36:40.455113Z",
     "iopub.status.idle": "2021-03-18T08:36:40.469361Z",
     "shell.execute_reply": "2021-03-18T08:36:40.470167Z"
    },
    "papermill": {
     "duration": 0.055836,
     "end_time": "2021-03-18T08:36:40.470425",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.414589",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\":\n",
    "    for name in names:\n",
    "        trains[name] = trains[name].sort(dim = 1)[0]\n",
    "        tests[name] = tests[name].sort(dim = 1)[0]\n",
    "        \n",
    "for name in names:\n",
    "    trains[name] = (trains[name]- mean_cifar)/std_cifar\n",
    "    tests[name] = (tests[name]- mean_cifar)/std_cifar\n",
    "    \n",
    "f_num = 30\n",
    "a_num=30\n",
    "\n",
    "trains_n = {}\n",
    "tests_n = {}\n",
    "for name in names:\n",
    "    trains_n[name] = trains[name].T.reshape(1000,f_num)[:,:a_num]\n",
    "    tests_n[name] = tests[name].T.reshape(1000,f_num)[:,:a_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "separate-technique",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.556415Z",
     "iopub.status.busy": "2021-03-18T08:36:40.555242Z",
     "iopub.status.idle": "2021-03-18T08:36:40.561142Z",
     "shell.execute_reply": "2021-03-18T08:36:40.559645Z"
    },
    "papermill": {
     "duration": 0.051652,
     "end_time": "2021-03-18T08:36:40.561448",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.509796",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_ex = split_index\n",
    "train = torch.cat((trains_n[\"teacher\"][:n_ex], tests_n[\"teacher\"][:n_ex]), dim = 0)\n",
    "y = torch.cat((torch.zeros(n_ex), torch.ones(n_ex)), dim = 0)\n",
    "\n",
    "rand=torch.randperm(y.shape[0])\n",
    "train = train[rand]\n",
    "y = y[rand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "european-elements",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.651340Z",
     "iopub.status.busy": "2021-03-18T08:36:40.650227Z",
     "iopub.status.idle": "2021-03-18T08:36:40.654105Z",
     "shell.execute_reply": "2021-03-18T08:36:40.654976Z"
    },
    "papermill": {
     "duration": 0.052054,
     "end_time": "2021-03-18T08:36:40.655259",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.603205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(a_num,100),nn.ReLU(),nn.Linear(100,1),nn.Tanh())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer =torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "funded-wilson",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.794908Z",
     "iopub.status.busy": "2021-03-18T08:36:40.789275Z",
     "iopub.status.idle": "2021-03-18T08:36:44.281678Z",
     "shell.execute_reply": "2021-03-18T08:36:44.282056Z"
    },
    "papermill": {
     "duration": 3.566447,
     "end_time": "2021-03-18T08:36:44.282193",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.715746",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c79693c134d4638a4183efff5cb5ca8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tqdm(range(1000)) as pbar:\n",
    "    for epoch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        inputs = train\n",
    "        outputs = model(inputs)\n",
    "        loss = -1 * ((2*y-1)*(outputs.squeeze(-1))).mean()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        pbar.set_description('loss {}'.format(loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-hindu",
   "metadata": {
    "papermill": {
     "duration": 0.081011,
     "end_time": "2021-03-18T08:36:44.397983",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.316972",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "framed-invitation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.509383Z",
     "iopub.status.busy": "2021-03-18T08:36:44.508775Z",
     "iopub.status.idle": "2021-03-18T08:36:44.510664Z",
     "shell.execute_reply": "2021-03-18T08:36:44.511124Z"
    },
    "papermill": {
     "duration": 0.063498,
     "end_time": "2021-03-18T08:36:44.511279",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.447781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_p(outputs_train, outputs_test):\n",
    "    pred_test = outputs_test[:,0].detach().cpu().numpy()\n",
    "    pred_train = outputs_train[:,0].detach().cpu().numpy()\n",
    "    tval, pval = ttest_ind(pred_test, pred_train, alternative=\"greater\", equal_var=False)\n",
    "    if pval < 0:\n",
    "        raise Exception(f\"p-value={pval}\")\n",
    "    return pval\n",
    "\n",
    "def get_p_values(num_ex, train, test, k):\n",
    "    total = train.shape[0]\n",
    "    sum_p = 0\n",
    "    p_values = []\n",
    "    positions_list = []\n",
    "    for i in range(k):\n",
    "        positions = torch.randperm(total)[:num_ex]\n",
    "        p_val = get_p(train[positions], test[positions])\n",
    "        positions_list.append(positions)\n",
    "        p_values.append(p_val)\n",
    "    return p_values\n",
    "\n",
    "def get_fischer(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return combine_pvalues(p_values, method=\"mudholkar_george\")[1]\n",
    "\n",
    "def get_max_p_value(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return max(p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "twenty-stations",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.590350Z",
     "iopub.status.busy": "2021-03-18T08:36:44.589910Z",
     "iopub.status.idle": "2021-03-18T08:36:44.602434Z",
     "shell.execute_reply": "2021-03-18T08:36:44.601810Z"
    },
    "papermill": {
     "duration": 0.046619,
     "end_time": "2021-03-18T08:36:44.602591",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.555972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outputs_tr = {}\n",
    "outputs_te = {}\n",
    "for name in names:\n",
    "    outputs_tr[name] = model(trains_n[name])\n",
    "    outputs_te[name] = model(tests_n[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "pretty-occurrence",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.676712Z",
     "iopub.status.busy": "2021-03-18T08:36:44.676246Z",
     "iopub.status.idle": "2021-03-18T08:36:44.678841Z",
     "shell.execute_reply": "2021-03-18T08:36:44.679300Z"
    },
    "papermill": {
     "duration": 0.038908,
     "end_time": "2021-03-18T08:36:44.679471",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.640563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "def print_inference(outputs_train, outputs_test):\n",
    "    m1, m2 = outputs_test[:,0].mean(), outputs_train[:,0].mean()\n",
    "    pval = get_p(outputs_train, outputs_test)\n",
    "    print(f\"p-value = {pval} \\t| Mean difference = {m1-m2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "australian-manitoba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.755920Z",
     "iopub.status.busy": "2021-03-18T08:36:44.755499Z",
     "iopub.status.idle": "2021-03-18T08:36:44.757574Z",
     "shell.execute_reply": "2021-03-18T08:36:44.757973Z"
    },
    "papermill": {
     "duration": 0.040486,
     "end_time": "2021-03-18T08:36:44.758100",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.717614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name in names:\n",
    "    outputs_tr[name], outputs_te[name] = outputs_tr[name][split_index:], outputs_te[name][split_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "failing-helena",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.830704Z",
     "iopub.status.busy": "2021-03-18T08:36:44.830280Z",
     "iopub.status.idle": "2021-03-18T08:36:44.843851Z",
     "shell.execute_reply": "2021-03-18T08:36:44.843018Z"
    },
    "papermill": {
     "duration": 0.048875,
     "end_time": "2021-03-18T08:36:44.844059",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.795184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teacher\n",
      "p-value = 0.0 \t| Mean difference = 1.9664628505706787\n",
      "distillation\n",
      "p-value = 1.6357332356378218e-109 \t| Mean difference = 1.0764296054840088\n",
      "extract-label\n",
      "p-value = 1.2185015221979737e-07 \t| Mean difference = 0.1020580530166626\n",
      "extract-logit\n",
      "p-value = 2.4135333217260383e-10 \t| Mean difference = 0.15179699659347534\n",
      "zero-shot\n",
      "p-value = 3.2766684441051306e-23 \t| Mean difference = 0.3881877064704895\n",
      "fine-tune\n",
      "p-value = 5.264759325597721e-174 \t| Mean difference = 1.4495909214019775\n",
      "pre-act-18\n",
      "p-value = 4.830257288283164e-166 \t| Mean difference = 1.4474780559539795\n",
      "independent\n",
      "p-value = 1.0 \t| Mean difference = -1.766313076019287\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(f\"{name}\")\n",
    "    print_inference(outputs_tr[name], outputs_te[name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-payment",
   "metadata": {
    "papermill": {
     "duration": 0.03092,
     "end_time": "2021-03-18T08:36:44.914273",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.883353",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Main Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "silver-yahoo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.995124Z",
     "iopub.status.busy": "2021-03-18T08:36:44.994443Z",
     "iopub.status.idle": "2021-03-18T08:36:45.111160Z",
     "shell.execute_reply": "2021-03-18T08:36:45.110046Z"
    },
    "papermill": {
     "duration": 0.166421,
     "end_time": "2021-03-18T08:36:45.111392",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.944971",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(f\"{dataset}_{v_type}.h5\"):\n",
    "    total_reps = 40\n",
    "    max_m = 45\n",
    "    total_inner_rep = 100\n",
    "\n",
    "    m_list = [x for x in range(2, max_m, 1)]\n",
    "    p_values_all_threat_models_dict = {}\n",
    "\n",
    "    n_pbar = tqdm(names, leave=False)\n",
    "    for name in n_pbar:\n",
    "        p_vals_per_rep_no = {}\n",
    "        r_pbar = tqdm(range(total_reps), leave=False)\n",
    "        for rep_no in r_pbar:\n",
    "            p_values_list = []\n",
    "            for m in m_list:\n",
    "                p_list = get_p_values(m, outputs_tr[name], outputs_te[name], total_inner_rep)\n",
    "                try:\n",
    "                    hm = hmean(p_list)\n",
    "                except:\n",
    "                    hm = 1.0\n",
    "                p_values_list.append(hm)\n",
    "                pbar.set_description(f\"{rep_no: 2d} {name} m={m: 3d}: f{hm: 0.5f}\")\n",
    "            r_pbar.set_description(f\"rep_no: {rep_no+1}/{total_reps}\")\n",
    "            p_vals_per_rep_no[rep_no] = p_values_list\n",
    "        p_values_all_threat_models_dict[name] = p_vals_per_rep_no\n",
    "        \n",
    "    df_list = []\n",
    "    for name, rep_dict in p_values_all_threat_models_dict.items():\n",
    "        df = pd.DataFrame(rep_dict).reset_index().assign(m=lambda r: r.index+2).drop([\"index\"], axis=1)\n",
    "        df_list.append(pd.melt(df,id_vars=[\"m\"], var_name=\"rep_no\", value_name=\"p_value\").assign(threat_model=name))\n",
    "    results_df = pd.concat(df_list)\n",
    "    \n",
    "    results_df.to_hdf(f\"{dataset}_{v_type}.h5\", v_type)\n",
    "    results_df\n",
    "else:\n",
    "    results_df = pd.read_hdf(f\"{dataset}_{v_type}.h5\", v_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-hamburg",
   "metadata": {
    "papermill": {
     "duration": 0.032145,
     "end_time": "2021-03-18T08:36:45.183579",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.151434",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "previous-clearing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.255351Z",
     "iopub.status.busy": "2021-03-18T08:36:45.254903Z",
     "iopub.status.idle": "2021-03-18T08:36:45.258220Z",
     "shell.execute_reply": "2021-03-18T08:36:45.258830Z"
    },
    "papermill": {
     "duration": 0.043358,
     "end_time": "2021-03-18T08:36:45.259029",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.215671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['text.latex.preamble'] = r'\\usepackage{times}'\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-attitude",
   "metadata": {
    "papermill": {
     "duration": 0.031796,
     "end_time": "2021-03-18T08:36:45.330702",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.298906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "engaging-theory",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.397708Z",
     "iopub.status.busy": "2021-03-18T08:36:45.397254Z",
     "iopub.status.idle": "2021-03-18T08:36:45.399815Z",
     "shell.execute_reply": "2021-03-18T08:36:45.399471Z"
    },
    "papermill": {
     "duration": 0.037914,
     "end_time": "2021-03-18T08:36:45.399912",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.361998",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rename_dict = {\n",
    "    \"threat_model\": \"Threat Model\", \n",
    "    \"teacher\": \"Source\", \n",
    "    \"distillation\": \"Distillation\", \n",
    "    \"extract-label\": \"Label-Query\", \n",
    "    \"extract-logit\": \"Logit-Query\", \n",
    "    \"zero-shot\": \"Zero-Shot Learning\",\n",
    "    \"fine-tune\": \"Fine-Tuning\",\n",
    "    \"pre-act-18\": \"Diff. Architecture\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "junior-being",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.474178Z",
     "iopub.status.busy": "2021-03-18T08:36:45.472912Z",
     "iopub.status.idle": "2021-03-18T08:36:45.505190Z",
     "shell.execute_reply": "2021-03-18T08:36:45.504197Z"
    },
    "papermill": {
     "duration": 0.068966,
     "end_time": "2021-03-18T08:36:45.505442",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.436476",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_df = (results_df.rename(columns=rename_dict).replace(rename_dict)\n",
    "           .query('(m >= 5)')\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "hourly-cloud",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.583822Z",
     "iopub.status.busy": "2021-03-18T08:36:45.583230Z",
     "iopub.status.idle": "2021-03-18T08:36:45.585764Z",
     "shell.execute_reply": "2021-03-18T08:36:45.586222Z"
    },
    "papermill": {
     "duration": 0.042938,
     "end_time": "2021-03-18T08:36:45.586376",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.543438",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sizes\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = SMALL_SIZE + 1\n",
    "BIGGER_SIZE = MEDIUM_SIZE + 1\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsi ze of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dirty-vulnerability",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.678558Z",
     "iopub.status.busy": "2021-03-18T08:36:45.677379Z",
     "iopub.status.idle": "2021-03-18T08:36:52.135072Z",
     "shell.execute_reply": "2021-03-18T08:36:52.135920Z"
    },
    "papermill": {
     "duration": 6.511977,
     "end_time": "2021-03-18T08:36:52.136195",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.624218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'p-value')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n",
    "g = sns.lineplot(x=\"m\", y=\"p_value\", hue=\"Threat Model\", data=plot_df, ax=ax, ci=99)\n",
    "g.axhline(0.05, ls='--', color=\"black\", alpha=0.4)\n",
    "g.axhline(0.01, ls='--', color=\"black\", alpha=0.4)\n",
    "g.set_xlabel(\"Number of Sample Revealed\")\n",
    "g.set_ylabel(\"p-value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-microphone",
   "metadata": {
    "papermill": {
     "duration": 0.034151,
     "end_time": "2021-03-18T08:36:52.210171",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.176020",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "opposed-inquiry",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.284920Z",
     "iopub.status.busy": "2021-03-18T08:36:52.284288Z",
     "iopub.status.idle": "2021-03-18T08:36:52.667422Z",
     "shell.execute_reply": "2021-03-18T08:36:52.666588Z"
    },
    "papermill": {
     "duration": 0.423721,
     "end_time": "2021-03-18T08:36:52.667630",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.243909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7ed2fcdfd204ef094cadacfbb4d7057",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import generate_table\n",
    "tab = generate_table(selected_m=10, outputs_te=outputs_te, outputs_tr=outputs_tr, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dedicated-proceeding",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.753650Z",
     "iopub.status.busy": "2021-03-18T08:36:52.752941Z",
     "iopub.status.idle": "2021-03-18T08:36:52.756744Z",
     "shell.execute_reply": "2021-03-18T08:36:52.756142Z"
    },
    "papermill": {
     "duration": 0.048732,
     "end_time": "2021-03-18T08:36:52.756901",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.708169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               mean_diff  p_value\n",
      "teacher            1.967    0.000\n",
      "distillation       1.098    0.000\n",
      "pre-act-18         1.471    0.000\n",
      "zero-shot          0.405    0.030\n",
      "fine-tune          1.423    0.000\n",
      "extract-label      0.107    0.170\n",
      "extract-logit      0.184    0.114\n",
      "independent       -1.793    1.000\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.float_format\", lambda x: f\"{x: 0.3f}\"):\n",
    "    print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "excess-plane",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.843959Z",
     "iopub.status.busy": "2021-03-18T08:36:52.840138Z",
     "iopub.status.idle": "2021-03-18T08:36:52.848269Z",
     "shell.execute_reply": "2021-03-18T08:36:52.847437Z"
    },
    "papermill": {
     "duration": 0.050581,
     "end_time": "2021-03-18T08:36:52.848479",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.797898",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} & mean_diff &    p_value \\\\\n",
      "\\midrule\n",
      "teacher       &     1.967 & $10^{-44}$ \\\\\n",
      "distillation  &     1.098 & $10^{-05}$ \\\\\n",
      "pre-act-18    &     1.471 & $10^{-14}$ \\\\\n",
      "zero-shot     &     0.405 & $10^{-02}$ \\\\\n",
      "fine-tune     &     1.423 & $10^{-10}$ \\\\\n",
      "extract-label &     0.107 & $10^{-01}$ \\\\\n",
      "extract-logit &     0.184 & $10^{-01}$ \\\\\n",
      "independent   &    -1.793 & $10^{+00}$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.float_format\", lambda x: f\"{x: 0.3f}\"):\n",
    "    print(tab.to_latex(formatters=dict(mean_diff=\"{:0.3f}\".format, p_value=lambda x: \"$10^{\"+f\"{x:0.0E}\"[2:]+\"}$\"), escape=False))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 16.022125,
   "end_time": "2021-03-18T08:36:53.503518",
   "environment_variables": {},
   "exception": null,
   "input_path": "./template.ipynb",
   "output_path": "./notebooks/CIFAR100_rand.ipynb",
   "parameters": {
    "dataset": "CIFAR100",
    "params_path": "/home/mohammad/transferability/src",
    "root_path": "/home/mohammad/transferability/files",
    "split_index": 500,
    "v_type": "rand"
   },
   "start_time": "2021-03-18T08:36:37.481393",
   "version": "2.3.3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "005a6ad3adb645c38bb40975ce114d53": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "0e6b696ebff743a691a526646c6334be": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "19033d45a471430f8fea54fae7b5eb8d": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "1ab5e9cbde2a4a8ab34a1a2cd14d075b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_1c3c26f5803844c0915092a8d52b554f",
       "placeholder": "​",
       "style": "IPY_MODEL_b9a9f7f315454cb681587bbda5979b9f",
       "value": "loss -0.994483470916748: 100%"
      }
     },
     "1c3c26f5803844c0915092a8d52b554f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "2c2f199af901445385f50f738ca4b622": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "33c7f6f0978a46278b0a48304da68996": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3415de4396d34269abe7b465ed27f822": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_005a6ad3adb645c38bb40975ce114d53",
       "max": 1000.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_3c3c581b18b6422ea05363a53fec99c3",
       "value": 1000.0
      }
     },
     "3c3c581b18b6422ea05363a53fec99c3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "4b43a459395145ed9c8d31964e933a7d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_0e6b696ebff743a691a526646c6334be",
       "placeholder": "​",
       "style": "IPY_MODEL_619212c76ed24cbbbe7b18f640b0d332",
       "value": "100%"
      }
     },
     "4c79693c134d4638a4183efff5cb5ca8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_1ab5e9cbde2a4a8ab34a1a2cd14d075b",
        "IPY_MODEL_3415de4396d34269abe7b465ed27f822",
        "IPY_MODEL_73cf298b3ea64febb8c0f4ffb177d950"
       ],
       "layout": "IPY_MODEL_19033d45a471430f8fea54fae7b5eb8d"
      }
     },
     "51e6955fdb79494091e6dd748b83961f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_610546b29e294a00b95f05f93e45b085",
       "placeholder": "​",
       "style": "IPY_MODEL_ff3562841cbc4fcaa074acfacb3fd77b",
       "value": " 8/8 [00:00&lt;00:00, 22.14it/s]"
      }
     },
     "610546b29e294a00b95f05f93e45b085": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "619212c76ed24cbbbe7b18f640b0d332": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "6b3176f90fcd420eaa1f45115ad2d1e7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "73cf298b3ea64febb8c0f4ffb177d950": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_6b3176f90fcd420eaa1f45115ad2d1e7",
       "placeholder": "​",
       "style": "IPY_MODEL_ce3a245f66cf4716bb48eee0f3e97873",
       "value": " 1000/1000 [00:03&lt;00:00, 378.02it/s]"
      }
     },
     "7e81a87585ff4e89bb39420115cda6e1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_33c7f6f0978a46278b0a48304da68996",
       "max": 8.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_8248f9fbed554027a1fd51d48608ea23",
       "value": 8.0
      }
     },
     "8248f9fbed554027a1fd51d48608ea23": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "b7ed2fcdfd204ef094cadacfbb4d7057": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_4b43a459395145ed9c8d31964e933a7d",
        "IPY_MODEL_7e81a87585ff4e89bb39420115cda6e1",
        "IPY_MODEL_51e6955fdb79494091e6dd748b83961f"
       ],
       "layout": "IPY_MODEL_2c2f199af901445385f50f738ca4b622"
      }
     },
     "b9a9f7f315454cb681587bbda5979b9f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "ce3a245f66cf4716bb48eee0f3e97873": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "ff3562841cbc4fcaa074acfacb3fd77b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}